# Internship - Machine Learning Research Engineer at Perplexity

Internship - Machine Learning Research Engineer at Perplexity in Berlin. Hybrid, full-time. Posted September 2, 2026.

- Company: Perplexity
- Location: Berlin
- Workplace: Hybrid
- Job type: Full-time
- Posted: 2026-09-02
- Apply: https://jobs.ashbyhq.com/perplexity/b9e1ff15-d52a-46d5-abf0-26460f2a116c/application
- Source: https://jobmagic.ai/jobs/internship-machine-learning-research-engineer-perplexity-fz7x7h

## Requirements

- Understanding of search and retrieval systems, including quality evaluation principles and metrics.
- Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models.
- Interested in representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization and robust evaluation.
- Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).

## Description

**Internship Program Berlin**
Internship program: 12 - 24 weeks, full-time, in-person in the Berlin office.
**Responsibilities**

- Relentlessly push search quality forward — through models, data, tools, or any other leverage available.

- Train, and optimize large-scale deep learning models using frameworks like PyTorch, leveraging distributed training (e.g., PyTorch Distributed, DeepSpeed, FSDP) and hardware acceleration, with a focus on retrieval and ranking models.

- Conduct research in representation learning, including contrastive learning, multilingual, evaluation, and multimodal modeling for search and retrieval.

- Build and optimize RAG pipelines for grounding and answer generation.

**Qualifications**

- Understanding of search and retrieval systems, including quality evaluation principles and metrics.

- Strong proficiency with PyTorch, including experience in distributed training techniques and performance optimization for large models.

- Interested in representation learning, including contrastive learning, dense & sparse vector representations, representation fusion, cross-lingual representation alignment, training data optimization and robust evaluation.

- Publication record in AI/ML conferences or workshops (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, SIGIR).